In the semiconductor manufacturing industry, semiconductor fabrication processes are used to make a wafer on which a plurality of integrated circuit dies are formed. A fabrication process is run to produce thousands (or more) parts and it is not uncommon to produce some dies that have irreparable defects or failures. The percentage of the integrated circuit dies produced that are operational is referred to as the “yield” of the process.
In order optimize the wafer/chip yield, a semiconductor manufacturer allocates engineers to analyze the yield of a process by grouping die failures into categories or classifications. Thus, one of the primary functions of a yield analysis engineer is to troubleshoot die failures, communicate the failure information and quantify the loss (impact on the yield) associated with the identified failure categories or classes. It is necessary to classify the failures before troubleshooting the failure causes.
Current failure classification techniques are completely manual. Engineers manually review failure pattern data to group wafer and die failures into categories and estimate yield loss based on the amount of loss that an engineer assigns to a lot. The amount of loss assigned to each failure class/category (also called a “detractor”) is based on the engineer's judgment, and therefore is subjective.
There are several problems with the current manual failure classification techniques. Because the yield analysis engineer uses his/her subjective judgment to assign a loss amount to a detractor, there is inherently a bias on the detractors. Classification becomes more difficult and the yield detractor analysis less accurate as the yield increases and the semiconductor fabrication process matures.